You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
85 lines
3.2 KiB
85 lines
3.2 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from dataclasses import dataclass
|
|
|
|
import paddle
|
|
|
|
from deepspeech.utils.log import Log
|
|
|
|
__all__ = ["UpdaterBase", "UpdaterState"]
|
|
|
|
logger = Log(__name__).getlog()
|
|
|
|
|
|
@dataclass
|
|
class UpdaterState:
|
|
iteration: int = 0
|
|
epoch: int = 0
|
|
|
|
|
|
class UpdaterBase():
|
|
"""An updater is the abstraction of how a model is trained given the
|
|
dataloader and the optimizer.
|
|
The `update_core` method is a step in the training loop with only necessary
|
|
operations (get a batch, forward and backward, update the parameters).
|
|
Other stuffs are made extensions. Visualization, saving, loading and
|
|
periodical validation and evaluation are not considered here.
|
|
But even in such simplist case, things are not that simple. There is an
|
|
attempt to standardize this process and requires only the model and
|
|
dataset and do all the stuffs automatically. But this may hurt flexibility.
|
|
If we assume a batch yield from the dataloader is just the input to the
|
|
model, we will find that some model requires more arguments, or just some
|
|
keyword arguments. But this prevents us from over-simplifying it.
|
|
From another perspective, the batch may includes not just the input, but
|
|
also the target. But the model's forward method may just need the input.
|
|
We can pass a dict or a super-long tuple to the model and let it pick what
|
|
it really needs. But this is an abuse of lazy interface.
|
|
After all, we care about how a model is trained. But just how the model is
|
|
used for inference. We want to control how a model is trained. We just
|
|
don't want to be messed up with other auxiliary code.
|
|
So the best practice is to define a model and define a updater for it.
|
|
"""
|
|
|
|
def __init__(self, init_state=None):
|
|
# init state
|
|
if init_state is None:
|
|
self.state = UpdaterState()
|
|
else:
|
|
self.state = init_state
|
|
|
|
def update(self, batch):
|
|
raise NotImplementedError(
|
|
"Implement your own `update` method for training a step.")
|
|
|
|
def state_dict(self):
|
|
state_dict = {
|
|
"epoch": self.state.epoch,
|
|
"iteration": self.state.iteration,
|
|
}
|
|
return state_dict
|
|
|
|
def set_state_dict(self, state_dict):
|
|
self.state.epoch = state_dict["epoch"]
|
|
self.state.iteration = state_dict["iteration"]
|
|
|
|
def save(self, path):
|
|
logger.debug(f"Saving to {path}.")
|
|
archive = self.state_dict()
|
|
paddle.save(archive, str(path))
|
|
|
|
def load(self, path):
|
|
logger.debug(f"Loading from {path}.")
|
|
archive = paddle.load(str(path))
|
|
self.set_state_dict(archive)
|